Computed tomography (CT) systems and methods are widely used, particularly for medical imaging and diagnosis. CT systems generally create images of one or more sectional slices through a subject's body. A radiation source, such as an X-ray source, irradiates the body from one side. At least one detector on the opposite side of the body receives radiation transmitted through the body. The attenuation of the radiation transmitted through the body is measured by processing electrical signals received from the detector.
A CT sinogram indicates attenuation through the body as a function of position along a detector array and as a function of the projection angle between the X-ray source and the detector array for various projection measurements. In a sinogram, the spatial dimensions refer to the position along the array of X-ray detectors. The time/angle dimension refers to the projection angle of X-rays, which changes as a function of time during a CT scan. The attenuation resulting from a portion of the imaged object (e.g., a vertebra) will trace out a sine wave around the vertical axis. Those portions farther from the axis of rotation correspond to sine waves with larger amplitudes, and the phases of the sine waves correspond to the angular positions of objects around the rotation axis. Performing an inverse Radon transform—or any other image reconstruction method—reconstructs an image from the projection data in the sinogram.
In clinical applications, a given sub-region within the body might have greater importance for a particular scan of a particular patient. For example, in interventional CT, a stent or other medical device might be inserted into a patient, and the region immediately surrounding the placement of the medical device is of primary importance. To achieve higher resolution in this area, a smaller diameter X-ray beam can be focused on the relevant region of interest for a CT scan. However, the reconstructed image from this smaller region of interest can result in truncation error. On the other hand, a reconstructed image with a larger field of view will either result in poorer resolution or require significantly more time and computational resources to reconstruct from the projection data.
In a CT scan, truncation error and artifacts result when a small diameter X-ray beam occupies less than the entire cross-section of a patient. Since incomplete data is available outside the region of interest (ROI) illuminated by the X-ray beam, the reconstruction can suffer from severe artifacts potentially rendering the image useless. Different approaches have been proposed to reduce these artifacts by estimating or determining data outside the ROI.
For example, a first category of algorithms attempts to overcome the ROI artifact by estimating the data outside the ROI. A technique can be used to extrapolate the truncated data. In some implementations, the extrapolation procedure can be incorporated into the convolution step of a filtered back-projection (FBP), or by using a smooth function to improve reconstruction inside the ROI. These estimated or eliminated projections may not model the objects outside the ROI accurately, resulting in residual artifacts. Moreover, these techniques do not provide image information outside the ROI, which image information can provide visual context for the image in the ROI, making it easier for clinical practitioners to interpret the reconstructed image.
Certain other methods of solving the truncation artifact problem use two passes, a first pass corresponding to a full field of view and a second pass using a limited or restricted field of view. For example, ROI image reconstruction can be performed by using iterative reconstruction (IR) by using two-pass IR and one projection subtraction in-between the two passes. Two options for implementing this method are: (i) a coarse grid size is applied in the first pass and a fine grid size is applied in the second pass; and (ii) a fine grid size is used in both passes, but a shrunken image volume is applied in the second-pass by reducing number of voxels. The two-pass method can decrease the truncation artifact, but at the cost of increased complexity and time to perform the second scan and perform additional reconstruction steps.
These extrapolation methods and two-pass methods fail to sufficiently mitigate the truncation artifact without increasing the computational time to reconstruct an image. However, increasing the computational time is not feasible in certain clinical applications when clinical practitioners rely on rapid feedback based in the imaging for task, such as positioning and arranging a stent or a medical device in a patient. Thus, an improved method of multiscale imaging is desired.